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Like most scientists, Chengping Chai is not content with the surface of things: He wants to probe beyond to learn what’s really going on. But in his case, he is literally building a map of the world beneath, using seismic and acoustic data that reveal when and where the earth moves.
In a discovery aimed at accelerating the development of process-advantaged crops for jet biofuels, scientists at ORNL developed a capability to insert multiple genes into plants in a single step.
Paul Langan will join ORNL in the spring as associate laboratory director for the Biological and Environmental Systems Science Directorate.
Seven scientists at the Department of Energy’s Oak Ridge National Laboratory have been named Battelle Distinguished Inventors, in recognition of their obtaining 14 or more patents during their careers at the lab.
While studying how bio-inspired materials might inform the design of next-generation computers, scientists at ORNL achieved a first-of-its-kind result that could have big implications for both edge computing and human health.
Although blockchain is best known for securing digital currency payments, researchers at the Department of Energy’s Oak Ridge National Laboratory are using it to track a different kind of exchange: It’s the first time blockchain has ever been used to validate communication among devices on the electric grid.
Eight ORNL scientists are among the world’s most highly cited researchers, according to a bibliometric analysis conducted by the scientific publication analytics firm Clarivate.
As the United States shifts away from fossil-fuel-burning cars and trucks, scientists at the Department of Energy’s Oak Ridge and Argonne national laboratories are exploring options for another form of transportation: trains. The research focuses on zero-carbon hydrogen and other low-carbon fuels as viable alternatives to diesel for the rail industry.
Laboratory Director Thomas Zacharia presented five Director’s Awards during Saturday night's annual Awards Night event hosted by UT-Battelle, which manages ORNL for the Department of Energy.
A new deep-learning framework developed at ORNL is speeding up the process of inspecting additively manufactured metal parts using X-ray computed tomography, or CT, while increasing the accuracy of the results. The reduced costs for time, labor, maintenance and energy are expected to accelerate expansion of additive manufacturing, or 3D printing.